MLB Wild Card win probability can look simple when it appears on a TV broadcast. A percentage pops up, the number changes after a home run, a pitching change, or a runner reaching second, and it can feel like the model is simply reacting to what everyone can already see. There is actually a lot more happening underneath that number.
Win probability is an attempt to translate the current state of a baseball game into a percentage representing the chance that each team eventually wins. Before the first pitch, that percentage is mostly based on expectations. Once the game starts, the model has to constantly combine those expectations with what has actually happened. The score changes, the inning changes, pitchers get tired, relievers become available or unavailable, benches get shorter, and managers make decisions that can completely change the remaining game.
The Wild Card round makes this especially interesting because postseason baseball does not always follow the same patterns as a normal regular-season game. Managers can use their best relievers earlier. Starting pitchers can have shorter leashes. Pinch hitters can appear in situations where they might have stayed on the bench in July. A single game can also put much more pressure on every decision because there may be little or no room to recover from a mistake.
For anyone interested in MLB analytics, understanding these changes is important. A good MLB Wild Card win probability model should not simply react to the score. It should understand the state of the game, the players available, the likely decisions coming next, and the uncertainty surrounding all of those factors.
This article breaks down how MLB Wild Card win probability works, which inputs matter most, how a practical model can be built, and how to interpret changing probabilities without treating them as guarantees.
Table Of Contents
What MLB Wild Card win probability really means for analysts
Data you actually need, not fluff
Build a practical modeling workflow
In-game updates without overfitting
Communicating uncertainty the way bettors and analysts use it
Conclusion
Related Posts
Frequently Asked Questions
What MLB Wild Card win probability really means for analysts
Pregame win probability vs. in-game win probability
Pregame win probability is an estimate created before the first pitch. At that point, the model does not know exactly how the game will unfold. Instead, it has to work with expectations about the starting pitchers, lineups, bullpen strength, defensive ability, park environment, weather, and other relevant conditions.
Think of pregame probability as the starting point for the model. If one team has an elite starting pitcher, a rested bullpen, a strong lineup against the opposing starter's handedness, and favorable defensive projections, those factors can push its initial probability higher.
In-game win probability is different because the model now has actual information from the game. The score is known. The number of outs is known. The runners on base are known. The current pitcher and batter are known. The model also has more information about bullpen usage and managerial decisions.
That means in-game probability can be thought of as the pregame expectation updated with new evidence.
If a team entered the game with a 56% pregame win probability and quickly falls behind 3-0, its probability should obviously decrease. But the size of that decrease depends on much more than the three runs. The inning matters. The number of outs matters. The opposing pitcher matters. The quality of the hitters coming up matters. The bullpen situation matters. The home or away status matters. Even the remaining bench options can matter.
This is why two teams with the same score can have very different win probabilities depending on the exact game state.
Why single-game variance is huge in Wild Card baseball
The Wild Card environment creates a situation where variance becomes especially important.
A regular-season team can lose a game and move on to the next one. In a Wild Card setting, the consequences of a single game can be much larger. That changes how managers deploy their players and how analysts should think about the game.
One unusual home run can change the entire state of a game. A defensive error can create extra baserunners without necessarily saying anything about the team's underlying ability. A pitcher can suddenly lose command. A normally reliable reliever can have a bad inning. A ball can find a gap instead of a fielder.
These events can create very large changes in win probability even though they do not necessarily indicate that one team is dramatically better than the other.
This is one of the most important ideas to understand when working with MLB Wild Card win probability. A probability model is not supposed to eliminate randomness. It is supposed to measure the probability of different outcomes while acknowledging that randomness exists.
A team with a 70% win probability does not have a 70% guarantee of winning. It means that under the model's assumptions, outcomes associated with that game state would produce a win for that team roughly 70% of the time over a large number of comparable situations.
That distinction becomes especially important in a short postseason sample.
Bullpen deployment changes the entire calculation
Bullpens are one of the biggest reasons Wild Card win probability is more complicated than a basic score-based model.
In the regular season, managers often have relatively predictable bullpen patterns. A setup reliever may normally work the eighth inning, while a closer is saved for the ninth. In a high-pressure postseason game, those patterns can change quickly.
A top reliever might enter in the sixth inning with runners on base. A closer might be used for more than three outs. A starting pitcher might be removed earlier than expected because the manager wants to avoid facing the opposing lineup a third time.
For a win probability model, this means the identity of the pitcher on the mound matters just as much as the inning.
Suppose a team is leading by one run in the seventh inning. The basic score may suggest that its probability is fairly strong. But what happens next depends heavily on which relievers remain available.
If the team's three best relievers have already pitched, the probability should account for the weaker options likely to handle the remaining innings. On the other hand, if the team has its best bullpen arms rested and ready, the same one-run lead can carry a different probability.
This is why bullpen availability should be treated as a dynamic input instead of a simple season-long team rating.
Pinch hitting and bench depth matter more than people think
Postseason managers can also become much more aggressive with pinch hitters.
If a pitcher is facing a dangerous hitter with a platoon advantage available on the bench, the expected outcome of that plate appearance can change quickly. The model needs to account for the possibility that the manager will make the substitution.
Bench depth therefore becomes part of the future state of the game.
It is not enough to ask who is batting right now. A stronger model asks who could be batting next, which substitutions are likely, and how those substitutions affect the expected scoring environment.
The same idea applies to pinch runners and defensive replacements. A team may sacrifice some offensive ability for improved defense late in the game, especially with a narrow lead. That decision changes the expected probability of future events.
Why the Wild Card sample should not be overfitted
One of the easiest mistakes in sports modeling is assuming that a small postseason sample automatically contains more useful information than a much larger regular-season sample.
It does not.
Postseason games are extremely important, but there are relatively few of them. A model that sees a handful of Wild Card games and tries to learn every possible postseason pattern can easily mistake randomness for a meaningful signal.
For example, if a certain team wins three consecutive games after using a closer in the eighth inning, that does not mean the eighth-inning closer strategy should automatically receive a huge weight.
The better approach is to use the larger body of baseball data as the foundation and then make carefully tested adjustments for postseason strategy, bullpen behavior, and managerial tendencies.
What should analysts use as the foundation?
A strong MLB Wild Card win probability model needs clear definitions for its inputs and outputs.
The basic building blocks include historical game results, play-by-play data, player performance, team strength, pitching quality, lineup composition, bullpen availability, park conditions, weather, defensive performance, and game-state information.
The important thing is not simply collecting as much data as possible. More data does not automatically create a better model.
The goal is to identify variables that have a reasonable connection to runs scored, runs prevented, or the probability of winning.
That keeps the model focused and makes it easier to determine whether a new feature actually improves predictive performance.
Data you actually need, not fluff
A useful MLB Wild Card win probability model starts with reliable data. The quality of the inputs matters because even a sophisticated algorithm can produce poor results when the underlying information is stale, incomplete, or incorrectly labeled.
Starting pitcher quality is one of the most important pregame inputs. A model should consider more than a basic earned run average. Strikeout rate, walk rate, quality of contact, velocity, pitch mix, recent workload, and expected performance indicators can all provide additional information.
Rest also matters. A starting pitcher working on normal rest is not necessarily equivalent to one being asked to perform under an unusual schedule. Recent pitch counts can provide additional context because workload can influence how long a pitcher is expected to remain effective.
The model should also consider bullpen freshness. This includes how many pitches key relievers threw recently, whether they worked on consecutive days, and which arms are realistically available.
Bullpen depth is particularly important during the Wild Card round because managers may use their strongest relievers in situations that would normally be handled by lower-leverage arms.
Lineup quality is another major input. A team's overall offensive rating is useful, but matchup-specific information can provide additional context. A lineup may perform differently against left-handed and right-handed pitching, and the opposing starter's handedness can influence how the batting order performs.
Contact quality can also help distinguish actual offensive strength from results that may have been influenced by luck. Metrics related to quality of contact, barrels, expected offensive production, strikeout rates, walk rates, and plate discipline can all contribute to a stronger offensive projection.
Park environment should not be ignored either. Different ballparks can produce different scoring environments, and weather can influence how the ball carries. Temperature, wind direction, wind speed, humidity, and roof status can all be relevant.
In a single postseason game, small environmental effects can become more noticeable because there are so few opportunities for the effects to average out.
Defense is another area that can quietly influence the model. Catcher receiving ability, throwing ability, infield range, outfield defense, and positioning can affect the expected result of close situations.
Travel and time-zone changes can be included as smaller adjustments, although they should generally receive less weight than major pitching, lineup, and bullpen factors.
Umpire tendencies can also be considered, but this is an area where models need to remain conservative. A strike zone tendency should not be treated as a guaranteed advantage. The effect is usually much smaller than the major inputs, and the uncertainty around estimating it should be reflected in the model.
The same philosophy applies to less obvious variables. A model should not add a feature simply because the information is available.
A variable such as day of the week might correlate with something in a historical dataset, but that does not mean it causes better predictions. Analysts need to separate meaningful information from patterns that appear because of noise.
Pregame data and in-game data are not the same thing
Before the game begins, the model is mostly working with projections.
Once the game starts, the data changes from projected information to observed information.
The current score, inning, outs, baserunners, pitcher, batter, pitch count, and substitutions become critical. The model also needs to understand which players remain available.
This creates a constantly changing state.
A starting pitcher who was projected to be excellent before the game may look different after throwing 85 pitches with declining velocity. A bullpen that looked deep before the game may become less reliable after several relievers are used.
The model therefore needs an updating system rather than a static pregame prediction.
Build a practical modeling workflow
A practical workflow begins with data ingestion.
Historical games should be organized so that each game has a consistent identifier, date, teams, park, starting pitchers, lineups, and relevant game conditions. Play-by-play events should then be connected to the game so that the model can recreate the state of the game at any point.
The historical dataset should contain enough regular-season information to establish reliable player and team baselines. Postseason games can then be used to study how strategies and usage patterns change under higher leverage.
Player information should be standardized as well. Player identifiers need to remain consistent across seasons so that the model does not accidentally treat the same player as multiple individuals.
The next step is feature engineering.
Instead of feeding raw data into a model and hoping it discovers everything, analysts can create features that represent meaningful baseball concepts.
A starting pitcher strength feature might combine expected run prevention, strikeout-minus-walk rate, quality of contact allowed, velocity trends, and rest.
A bullpen depth feature can estimate how many high-quality relief innings a team can realistically expect from its available pitchers.
A lineup matchup feature can represent projected offensive strength against the handedness of the opposing pitcher.
A weather feature can combine temperature, wind, humidity, and roof status into an estimated scoring adjustment.
An umpire feature can represent expected called-strike tendencies relative to the league baseline.
These features do not need to be perfect. They need to be stable, measurable, and useful.
Start with a calibrated baseline
A logistic regression model is a strong starting point because it is relatively simple and interpretable.
The objective is to estimate the probability of a win from the pregame features.
More advanced models can be added later, including gradient boosting approaches. However, complexity should not be the first goal.
A simple model provides a benchmark. If a complicated model cannot consistently outperform the baseline, there may not be a good reason to use the additional complexity.
Calibration is especially important.
A model that predicts 70% should actually win around 70% of the time over a sufficiently large group of comparable predictions.
A model that predicts 90% but only wins 75% of those situations is not well calibrated, even if its ranking of teams is sometimes useful.
Brier score and log loss can help evaluate probability quality. Calibration curves can also show whether predictions systematically become too aggressive near the extremes.
Monte Carlo simulation adds another layer
Once a reliable baseline exists, simulation can help model the chain of events that can happen during a game.
A Monte Carlo approach can simulate thousands of possible game paths.
Each simulation can begin with the projected starting pitchers, lineups, park environment, weather, and bullpen availability. The game then progresses through simulated plate appearances based on expected player performance.
As the simulated game moves forward, the model can introduce realistic managerial decisions.
A starting pitcher who becomes less effective can be removed. A high-leverage reliever can enter. A pinch hitter can replace a weak matchup. A closer can be used for multiple outs when the situation calls for it.
After thousands of simulations, the percentage of simulated games won by each team becomes the estimated win probability.
The benefit is that the model can account for many possible paths instead of treating the game as a simple average.
The important word here is possible. Simulation is only as good as the assumptions driving it.
If the bullpen decision rules are unrealistic, running 20,000 simulations does not fix the problem. It simply creates 20,000 versions of the same flawed assumptions.
Use conservative priors
Wild Card baseball is not a good environment for pretending the model knows everything.
A useful approach is to shrink extreme predictions toward a more reasonable center when the inputs are uncertain.
If the model produces a 91% pregame probability but the starting lineup has not been confirmed, the weather is uncertain, and several bullpen arms may be unavailable, that 91% figure should be treated cautiously.
The exact shrinkage method can vary, but the principle is consistent. Uncertainty should influence how aggressively the model expresses its prediction.
Market information can also be used as a calibration reference without allowing it to completely replace the model. The purpose is to identify situations where the model and market differ while recognizing that neither side is guaranteed to be correct.
Update the model throughout the day
A useful operational workflow starts well before the game.
At roughly a day before first pitch, the model can generate an initial estimate using probable starters, projected lineups, current bullpen information, and an early weather outlook.
Several hours before the game, confirmed starters and more reliable weather information can improve the estimate.
Closer to first pitch, the model should incorporate confirmed lineups, final weather information, roof status when relevant, and the latest bullpen availability.
The final pregame probability should therefore be treated as a time-stamped estimate rather than a permanent number.
Once the game begins, the live model takes over.
In-game updates without overfitting
The base-out state is one of the most important concepts in live MLB win probability.
A runner on first with no outs is not equivalent to a runner on first with two outs. A runner on third with one out is not equivalent to empty bases with one out.
Each combination of runners and outs creates a different scoring environment.
Run expectancy provides a useful foundation for understanding these situations.
However, a basic league-average run expectancy matrix can be improved by considering the actual environment of the game.
If the park and weather are producing an unusually strong home run environment, expected scoring can be adjusted upward. If an elite pitcher is on the mound, expected scoring can be adjusted downward.
The same applies to the quality of the hitters coming to the plate.
A lineup with several above-average hitters due up should produce a different expectation from the bottom of the batting order.
The model should also monitor the pitcher.
Pitch count alone is not enough. Velocity changes, command, swing-and-miss performance, and contact quality can provide information about whether the pitcher is performing close to expectations.
However, live adjustments need to remain conservative.
If a pitcher loses a mile per hour on one pitch, that should not automatically cause a dramatic change. Small fluctuations are normal. A persistent velocity decline combined with command problems and hard contact is much more meaningful.
Bullpen availability must be updated continuously
Once the game reaches the later innings, bullpen information can become one of the most important parts of the model.
A team that has its best relievers available has a different future outlook from a team that has already used those pitchers.
The model should track who has pitched, how many pitches they threw, when they last appeared, and whether they are likely to be available again.
Warm-up information can be useful when available, but the model should not assume that every pitcher warming up will definitely enter the game.
Managerial tendencies can help estimate the probability of a particular reliever being used.
This is where postseason strategy becomes especially important.
A manager may bring a top reliever into a tie game in the seventh inning because the upcoming hitters are particularly dangerous. That decision can significantly alter the future game state.
Smooth the probability instead of creating unnecessary whiplash
Live win probability should move when meaningful events happen, but it should not react wildly to every small event.
A routine ball or strike usually should not create a massive change.
A single with runners on base can create a larger shift because it changes the base-out state. A home run can create a major change because it directly changes the score and eliminates future opportunities for the opposing team to respond in that inning.
An injury to a starting pitcher can create an even larger structural shift because it changes who is available to pitch the remaining innings.
Smoothing can help prevent unnecessary volatility.
The model can use techniques similar to exponential smoothing to prevent tiny changes from creating distracting jumps while still allowing meaningful events to produce meaningful movements.
Extreme values should also be treated carefully. A model reaching 99% in the fifth inning may be expressing more certainty than the underlying information supports.
The probability should generally remain within reasonable limits until the game state becomes highly decisive.
Backtesting is where the model earns credibility
A model should not be judged by how convincing its live numbers look.
It should be tested against historical games.
Historical Wild Card and postseason games can be replayed using their actual play-by-play sequence. At each point in the game, the model can generate a win probability using only the information that would have been available at that moment.
The resulting predictions can then be compared with actual outcomes.
Calibration is one of the most important tests.
If the model produces probabilities between 70% and 80%, the actual win rate in that group should eventually move toward the same range.
Sharpness is also useful. A model that predicts 50% for almost every game may be perfectly cautious, but it is not very informative.
The goal is to find the balance between useful separation and accurate calibration.
Communicating uncertainty the way bettors and analysts use it
A win probability number is easier to understand when the model also explains why it produced that number.
Instead of simply displaying 63%, a useful system can identify the major factors behind the estimate.
The explanation might mention starting pitcher quality, bullpen availability, lineup matchup, and park conditions.
That makes the probability easier to evaluate without pretending the model has perfect knowledge.
Confidence intervals can also help.
An 80% interval communicates that there is uncertainty around the central estimate. This is particularly valuable before a Wild Card game because many variables are not completely settled.
If the model has a 58% estimate with a relatively wide interval, the appropriate interpretation is different from a situation where the model has the same central estimate with much narrower uncertainty.
The number itself is not the entire story.
Scenario analysis makes probability more useful
One of the most practical features of a strong model is the ability to ask what happens if something changes.
What happens if the starting pitcher exits after four innings?
What happens if the roof closes?
What happens if the opposing team uses its closer for four outs?
What happens if a key reliever becomes unavailable?
These scenarios can be simulated before or during the game.
The model can then show how the probability changes under each situation.
This does not make the prediction certain. It simply makes the model more transparent.
Where ATSwins fits into the process
ATSwins is an AI-powered sports prediction platform focused on data-driven sports analysis across MLB and other major sports. Its approach includes areas such as predictions, player props, betting splits, and profit tracking.
For MLB Wild Card analysis, the useful idea is to treat model information as part of a broader decision-making process rather than relying on one percentage in isolation.
A pregame win probability can provide a baseline view of a matchup. Player projections and market information can provide additional context. Live developments can then change the expected game environment.
Tracking those decisions over time is also valuable because it creates a record of what information actually contributed to a decision.
That kind of record can help separate a repeatable analytical process from decisions that only looked good after the fact.
The key is to keep the process disciplined. A model should not be used to create false certainty, and a probability should not be treated as a guarantee.
Practical pregame workflow
Before the game, confirm the starting pitchers and their rest situations. Check which major bullpen arms are realistically available. Confirm the projected lineup and identify important platoon matchups.
Then update the park and weather environment.
After that, calculate the baseline probability and run the simulation layer if the model uses one. Produce the central probability and the uncertainty range.
Finally, compare the result with the available market information and review any major disagreement.
The important part is not simply finding a difference. The analyst needs to understand why the difference exists.
If the model is higher because it believes a bullpen is significantly healthier than the market assumes, that is a testable explanation.
If the difference comes from an obscure feature that has barely been validated, it deserves much more caution.
Practical in-game workflow
The live process begins with the final pregame probability.
After each meaningful event, update the base-out state, score, inning, pitcher, batter, and bullpen availability.
The model then estimates the expected future run environment.
If the leverage situation changes, reassess which pitchers and hitters are likely to be used.
If the starter's performance changes materially, update the pitcher projection.
If the model detects a structural change, such as an injury or major pitching substitution, allow a larger adjustment.
Otherwise, keep small changes small.
Every update should be logged so the analyst can later understand why the probability moved.
Common modeling mistakes
One of the biggest mistakes is treating postseason baseball exactly like regular-season baseball. Managers change their behavior when the game carries more weight, so bullpen usage and pitcher hooks need to be modeled accordingly.
Another mistake is overreacting to tiny batter-versus-pitcher samples. A hitter going three-for-seven against a particular pitcher does not automatically mean the hitter has a meaningful advantage. Small samples are extremely noisy.
Ignoring weather is another problem, especially in parks where wind can influence home runs and extra-base hits.
Another common issue is probability whiplash. If the model changes dramatically after every ball and strike, users may lose confidence in the output even when the underlying methodology is sound.
Data staleness can also cause major problems. A lineup change, pitching scratch, or bullpen availability change can make an otherwise strong pregame estimate outdated.
The solution is not necessarily a more complicated model. Often, the solution is better data validation and faster updates.
Conclusion
MLB Wild Card win probability is much more than a number that changes when the score changes. The pregame estimate begins with expectations about starting pitchers, lineups, bullpens, park conditions, weather, defense, and other factors that influence the expected run environment.
Once the game begins, those expectations have to be combined with actual events. The score, inning, outs, baserunners, current pitcher, batter matchups, bullpen availability, and managerial decisions all become part of the calculation.
The Wild Card environment makes this especially challenging because managers can change normal usage patterns. Starting pitchers can have shorter leashes, high-leverage relievers can appear earlier, and bench players can become important much sooner than they would during an ordinary regular-season game.
A practical model should therefore begin with a calibrated baseline, add context carefully, and use simulation when the additional complexity has been validated. It should also respect uncertainty instead of trying to eliminate it.
For analysts and bettors using ATSwins, the most useful approach is to view MLB Wild Card win probability as one piece of a broader analytical process. A probability can help describe the current state of a game, while player projections, matchup information, market prices, bullpen conditions, and historical performance can provide additional context.
The goal is not to pretend that baseball can be predicted perfectly. It cannot. The goal is to build a process that responds to meaningful information, avoids unnecessary overreaction, and remains calibrated over a large sample.
That is ultimately what makes win probability useful. It does not tell you exactly what will happen next. It gives you a structured way to understand what the current game state means, how much uncertainty remains, and which changes are actually important.
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For more MLB analytics, sports prediction, player prop analysis, and data-driven sports insights, visit ATSwins.ai.
Frequently Asked Questions
What is MLB Wild Card win probability in simple terms?
MLB Wild Card win probability is an estimate of the chance that a team wins the game from a particular point in time. Before the first pitch, the estimate is based primarily on expected team and player performance, starting pitching, lineups, bullpen strength, park conditions, weather, and other available information.
Once the game begins, the probability changes based on the actual game state. The score, inning, number of outs, runners on base, current pitcher, batter matchup, and remaining bullpen options all become important.
For example, a team that begins with a 55% probability may move significantly higher after scoring multiple runs. However, the exact size of the movement depends on when those runs occur and what remains in the game.
What factors have the biggest effect on MLB Wild Card win probability before the game?
Starting pitcher quality is one of the most important factors. The model should consider underlying performance, strikeout and walk rates, quality of contact, velocity, pitch mix, workload, and rest.
Bullpen availability can also have a major effect, especially in a postseason game where managers may use their strongest relievers aggressively.
Lineup quality and platoon matchups are also important. A lineup that matches up well against the opposing starter can improve its expected offensive performance.
Park and weather conditions provide additional context. Wind, temperature, roof status, and the characteristics of the ballpark can influence scoring and home run probability.
Defense, catcher ability, travel, and umpire tendencies can contribute as smaller adjustments, although they generally should not overwhelm the larger signals.
Why can MLB Wild Card win probability change so quickly during a game?
Baseball is highly state-dependent.
A runner reaching second with no outs creates a different scoring situation from an empty-base situation. A one-run lead in the third inning is also very different from a one-run lead in the ninth.
As the game moves into the later innings, the available players become increasingly important. A team may have already used its best relievers, while the opposing team may still have several high-leverage arms available.
A home run can create a large probability change because it immediately changes the score and eliminates some future opportunities for the opposing team.
Should win probability be treated as a prediction or a guarantee?
It should be treated as a probability, not a guarantee.
If a model gives a team a 70% win probability, that does not mean the team is guaranteed to win. It means that comparable situations, assuming the model is calibrated, should result in wins for that team approximately 70% of the time over a sufficiently large sample.
A 30% outcome is still possible. In fact, a model that never allows unexpected results would not be modeling baseball realistically.
This is why calibration is so important. The probability needs to correspond reasonably well with actual historical outcomes.
How does bullpen availability affect Wild Card win probability?
Bullpen availability can substantially change the expected outcome of the remaining innings.
If a team has its strongest relievers rested and available, the model can assign stronger run-prevention expectations to those future innings.
If those same pitchers have already worked, the model may need to expect lower-quality relief options later.
This effect becomes especially important in the Wild Card round because managers can deploy their best relievers earlier than they normally would.
Why should models be careful with postseason samples?
There are not enough postseason games to safely assume that every pattern observed in the playoffs represents a stable relationship.
A team winning several games with a particular bullpen strategy does not necessarily prove that the strategy itself caused those wins.
Small samples contain substantial randomness.
A stronger modeling approach uses a large historical dataset to establish the underlying player and team relationships, then applies carefully tested postseason adjustments.
How can analysts tell whether a win probability model is calibrated?
One common method is to group predictions into probability ranges and compare the predicted probabilities with actual win rates.
For example, if a model makes many predictions between 70% and 80%, the actual win rate for those predictions should eventually move toward that range.
Brier score and log loss are also useful for evaluating probabilistic predictions.
Calibration should be tested on data that was not used to train the model. Otherwise, the evaluation can look better than the model actually performs in new situations.
How can ATSwins help with MLB Wild Card analysis?
ATSwins provides data-driven sports analysis that includes MLB predictions, player props, betting splits, and profit tracking. These tools can provide additional context when evaluating a matchup.
The most useful approach is to combine different pieces of information rather than relying on a single number. Win probability can describe the expected game state, while player projections, matchup information, market prices, and bullpen conditions can provide additional context.